This project demonstrates a complete workflow for chest X-ray cancer classification utilizing the power of Machine Learning and MLflow for experiment tracking and logging. By leveraging readily available chest X-ray dataset and state-of-the-art deep learning models to build a robust and accurate classification system. MLflow seamlessly integrates into the process, capturing experiment details, model metrics, and artifacts, enabling reproducibility and insightful analysis.
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Pipeline Tracking : DVC implemented to track artifacts
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FastAPI : Developed API using FastAPI for inference
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MLflow : USed Mlflow to perform experiment tracking.
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Clone the repository:
git clone https://github.com/pks916/endtoendml.git
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Create new environment
conda create -n endtoendml
- Activate the environment
conda activate endtoendml
- Libraries installation & Setup
pip install -r requirements.txt
uvicorn routes:app
